Image quantization using improved artificial fish swarm algorithm

作     者：El-said, Shaimaa Ahmed 

作者机构：Zagazig Univ Fac Engn Elect & Commun Dept Zagazig Egypt 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2015年第19卷第9期

页      面：2667-2679页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Image quantization Compression Data clustering FCM Swarm intelligence Artificial fish swarm algorithm (AFSA) 

摘      要：Most image compression algorithms suffer from several drawbacks: high-computational complexity, moderate reconstructed picture qualities, and a variable bit rate. In this paper, an efficient color image quantization technique that depends on an optimized Fuzzy C-means (OFCM) algorithm is proposed. It exploits the optimization capability of the improved artificial fish swarm algorithm to overcome the shortage of Fuzzy C-means algorithm. It uses error diffusion algorithms to obtain perceptually better images after quantization. Experiments are carried out to estimate the performance of the proposed OFCM algorithm in image compression using standard image set. The results indicate that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction. The performance efficiency of the proposed technique is compared with those of three other quantization algorithms. The Comparative results confirmed that the OFCM has potential in terms of both accuracy and perceptual quality as compared to recent methods of the literature.